@inproceedings{c93a3a719d4340f6a37960d958fc0656,
title = "An Optimized Multi-sensor Fused Object Detection Method for Intelligent Vehicles",
abstract = "An accurate and efficient environment perception system is crucial for intelligent vehicles. This study proposes an optimized 2D object detection method utilizing multi-sensor fusion to improve the performance of the environment perception system. In the sensor fusion module, a depth completion network is used to predict dense depth map, so both dense and sparse RGB-D images can be obtained. Then, an efficient object detection baseline is optimized for intelligent vehicles. This method is verified by KITTI 2D object detection dataset. The experimental results show that the proposed method can be more accurate than many latest methods on KITTI leaderboard. Meanwhile, this method consumes less inference time and shows its high efficiency.",
keywords = "2D object detection, deep learning, multi-sensor fusion",
author = "Jiayu Shen and Qingxiao Liu and Huiyan Chen",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 5th IEEE International Conference on Intelligent Transportation Engineering, ICITE 2020 ; Conference date: 11-09-2020 Through 13-09-2020",
year = "2020",
month = sep,
doi = "10.1109/ICITE50838.2020.9231355",
language = "English",
series = "2020 IEEE 5th International Conference on Intelligent Transportation Engineering, ICITE 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "265--270",
booktitle = "2020 IEEE 5th International Conference on Intelligent Transportation Engineering, ICITE 2020",
address = "United States",
}